Efficient Rotation-Scaling-Translation Parameter Estimation Based on the Fractal Image Model

Mikhail L. Uss (1) , Benoit Vozel (2, 3) , Vladimir V. Lukin (1) , Kacem Chehdi (3)

1 Taras Shevchenko National University of Kyiv
2 ENSSAT - École Nationale Supérieure des Sciences Appliquées et de Technologie
3 IETR - Institut d'Électronique et des Technologies du numéRique

This paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator MLfBm (ML stands for "maximum likelihood" and fBm stands for "fractal Brownian motion") has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the MLfBm estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75-2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for MLfBm estimates can be obtained from the Cramer-Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameters

Mots clés: Area-based image registration; Artifacts; Cramer-Rao lower bound (CRLB); Fisher information; fractional Brownian motion model; Hyperion; hyperspectral imagery; intensity; isometric scaling; Landsat 8; maximum-likelihood estimation (MLE); mutual-information; Performance; performance limits; registration; rotation; signal-dependent noise; subpixel registration; translation

Domaines: Traitement du signal et de l'image [eess.SP]; Réseaux et télécommunications [cs.NI]

Mikhail L. Uss, Benoit Vozel, Vladimir V. Lukin, Kacem Chehdi. Efficient Rotation-Scaling-Translation Parameter Estimation Based on the Fractal Image Model. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (1), pp.197-212. ⟨10.1109/TGRS.2015.2453126⟩. ⟨hal-01300877⟩